Literature DB >> 12511696

Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect.

Priya Kumar Shah1, John H M Austin, Charles S White, Pavni Patel, Linda B Haramati, Gregory D N Pearson, Maria C Shiau, Yahya M Berkmen.   

Abstract

PURPOSE: To assess for change in the 1990s in the failure of detection at chest radiography of potentially resectable non-small cell lung cancer (NSCLC) lesions compared with experience in the previous decade.
MATERIALS AND METHODS: From 1993 to 2001, an observational cohort was identified that consisted of 40 instances of NSCLC evident retrospectively at chest radiography but undetected by a radiologist at a time when the cancer was potentially resectable for cure. Sizes and locations of the tumors were assessed. Pearson chi(2) testing was performed to compare the sex distribution of lung cancer in the present series with population data for the sex distribution of lung cancer in the United States during the present study.
RESULTS: Twenty-five (62%) undetected NSCLCs were in men and 15 (38%) were in women, yielding a ratio not significantly different from that for the sex distribution of NSCLC according to national data (chi(2) = 0.22, P =.64). Median patient age was 62 years (range, 37-87 years). Median diameter of the missed cancers was 1.9 cm. Missed cancers were most commonly located in the upper lobes (right, 45%; left, 28%; total, 72%), especially in the apical and posterior segments/subsegments (60% of all the missed cancers). A clavicle obscured 22% of the missed cancers. Eighty-five percent of the missed cancers were in peripheral locations.
CONCLUSION: Potentially resectable NSCLC lesions missed at chest radiography were characterized by predominantly peripheral (85%) and upper lobe (72%) locations and by apical and posterior segmental/subsegmental locations in an upper lobe (60%). Distribution by sex of the missed cancers was comparable to national data for NSCLC. The missed cancers had a median diameter of 1.9 cm. Copyright RSNA, 2003

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Year:  2003        PMID: 12511696     DOI: 10.1148/radiol.2261011924

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  41 in total

1.  Effects of dual-energy subtraction chest radiography on detection of small pulmonary nodules with varying attenuation: receiver operating characteristic analysis using a phantom study.

Authors:  Seitaro Oda; Kazuo Awai; Yoshinori Funama; Daisuke Utsunomiya; Yumi Yanaga; Koichi Kawanaka; Yasuyuki Yamashita
Journal:  Jpn J Radiol       Date:  2010-05-01       Impact factor: 2.374

2.  Spectrum of diagnostic errors in radiology.

Authors:  Antonio Pinto; Luca Brunese
Journal:  World J Radiol       Date:  2010-10-28

3.  A novel bone suppression method that improves lung nodule detection : Suppressing dedicated bone shadows in radiographs while preserving the remaining signal.

Authors:  Jens von Berg; Stewart Young; Heike Carolus; Robin Wolz; Axel Saalbach; Alberto Hidalgo; Ana Giménez; Tomás Franquet
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-09-04       Impact factor: 2.924

Review 4.  Errors in imaging patients in the emergency setting.

Authors:  Antonio Pinto; Alfonso Reginelli; Fabio Pinto; Giuseppe Lo Re; Federico Midiri; Carlo Muzj; Luigia Romano; Luca Brunese
Journal:  Br J Radiol       Date:  2016-02-03       Impact factor: 3.039

5.  Negative chest X-rays in primary care patients with lung cancer.

Authors:  Sally Stapley; Deborah Sharp; William Hamilton
Journal:  Br J Gen Pract       Date:  2006-08       Impact factor: 5.386

6.  Integration of temporal subtraction and nodule detection system for digital chest radiographs into picture archiving and communication system (PACS): four-year experience.

Authors:  Shuji Sakai; Hidetake Yabuuchi; Yoshio Matsuo; Takashi Okafuji; Takeshi Kamitani; Hiroshi Honda; Keiji Yamamoto; Keiichi Fujiwara; Naoki Sugiyama; Kunio Doi
Journal:  J Digit Imaging       Date:  2007-03-01       Impact factor: 4.056

7.  Effect of multiscale processing in digital chest radiography on automated detection of lung nodule with a computer assistance system.

Authors:  Qian He; Wen He; Keyang Wang; Daqing Ma
Journal:  J Digit Imaging       Date:  2008-02-01       Impact factor: 4.056

8.  Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification.

Authors:  Sheng Chen; Kenji Suzuki; Heber MacMahon
Journal:  Med Phys       Date:  2011-04       Impact factor: 4.071

9.  Pulmonary nodule size evaluation with chest tomosynthesis and CT: a phantom study.

Authors:  S S Shim; Y-W Oh; K A Kong; Y J Ryu; Y Kim; D H Jang
Journal:  Br J Radiol       Date:  2015-01-21       Impact factor: 3.039

Review 10.  Overview of deep learning in medical imaging.

Authors:  Kenji Suzuki
Journal:  Radiol Phys Technol       Date:  2017-07-08
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